Apprenticeship

This part of my life is called "The Pursuit of Doctorate". Here is my blog about learning process.

Diving In

• Privacy protection

• Federated learning

• Fairness

• Blockchain

• Others

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Legend:

📒	📝	⌨️	📷	💾
PDF Files	Notes	Code	Slides	Other Supplementaries

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Awesome Privacy Protection Research Materials

A curated list of awesome privacy protection research materials.

• Books

Courses:

• Tutorial

Papers:

• Policy Brief
• Survey
• Basic Techniques
• Private Framework
• Private Benchmark
• Private Data Publishing
  • Transaction Data Publishing
  • Streaming Data Publishing
  • Graph Data Publishing
  • Image Data Publishing
• Private Data Analysis
  • Private Learning
  • Frequent Itemset Mining
  • Graph Data Analysis
  • Recommender Systems
  • Survival Analysis
  • Natural Language Processing
• Machine Unlearning
• Cryptography
  • Oblivious Transfer
  • Searchable Encryption
  • Encrypted Database
  • Encrypted Retrieval
  • Encrypted Inference

🔝

Books

Title	Authors	Published in	Year	Files	Notes	Supplementaries
数据库系统概论	王珊, 萨师煊	高等教育出版社	2014		📝

• | The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth | TCS | 2014 | [:ledger:](https://www.nowpublishers.com/article/Details/TCS-042) | | [:floppy_disk:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) |

• Jordi Soria-Comas, Josep Domingo-Ferrer: Optimal data-independent noise for differential privacy. Inf. Sci. 250: 200-214 (2013)

• Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu: Differential Privacy and Applications. Advances in Information Security 69, Springer 2017, ISBN 978-3-319-62002-2, pp. 1-222

• Ninghui Li, Min Lyu, Dong Su, Weining Yang: Differential Privacy: From Theory to Practice. Synthesis Lectures on Information Security, Privacy, & Trust, Morgan & Claypool Publishers 2016, pp. 1-138

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
The U.S. Census Bureau Adopts Differential Privacy	John M. Abowd	U.S. Census Bureau	KDD 2018	📒		📷

• | The Algorithmic Foundations of Data Privacy | Aaron Roth | Penn | Fall 2011 | [:ledger:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) | | [:floppy_disk:](https://www.nowpublishers.com/article/Details/TCS-042) |

🔝

Policy Brief

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	China's Social Credit System: A Mark of Progress or a Threat to Privacy?	Martin Chorzempa, Paul Triolo, Samm Sacks		2018	📒	/	💾

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Differentially Private Data Publishing and Analysis: A Survey	Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu	TKDE	2017	📒
	Privacy for Recommender Systems: Tutorial Abstract	Bart P. Knijnenburg, Shlomo Berkovsky	RecSys	2017	📒		📷
	Privacy in Location-Based Services: State-of-the-Art and Research Directions	Mohamed F. Mokbel	MDM	2007	📒		📷

🔝

Basic Techniques

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms	Fengxiang He, Bohan Wang, Dacheng Tao	UAI	2021	📒	📝
OSDP	One-sided Differential Privacy	Ios Kotsogiannis, Stelios Doudalis, Samuel Haney, Ashwin Machanavajjhala, Sharad Mehrotra	ICDE	2020	📒		📷📷
SVT-S	Understanding the Sparse Vector Technique for Differential Privacy	Min Lyu, Dong Su, Ninghui Li	VLDB	2017	📒
	Nearly-Optimal Private LASSO	Kunal Talwar, Abhradeep Thakurta, Li Zhang	NIPS	2015	📒	📝	📒
	Privacy-preserving statistical estimation with optimal convergence rates	Adam D. Smith	STOC	2011	📒
	Smooth sensitivity and sampling in private data analysis	Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith	STOC	2007	📒

🔝

Private Framework

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
KTELO	KTELO: A Framework for Defining Differentially-Private Computations	Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau	SIGMOD	2018	📒		⌨️

• PINQ: Frank McSherry: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. SIGMOD Conference 2009: 19-30; Davide Proserpio, Sharon Goldberg, Frank McSherry: Calibrating Data to Sensitivity in Private Data Analysis. PVLDB 7(8): 637-648 (2014)
• Fuzz: Marco Gaboardi, Andreas Haeberlen, Justin Hsu, Arjun Narayan, Benjamin C. Pierce: Linear dependent types for differential privacy. POPL 2013: 357-370
• PrivInfer: Gilles Barthe, Gian Pietro Farina, Marco Gaboardi, Emilio Jesús Gallego Arias, Andy Gordon, Justin Hsu, Pierre-Yves Strub: Differentially Private Bayesian Programming. ACM Conference on Computer and Communications Security 2016: 68-79
• LightDP: Danfeng Zhang, Daniel Kifer: LightDP: towards automating differential privacy proofs. POPL 2017: 888-901

🔝

Private Benchmark

• DPBench: Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang: Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154

🔝

Private Data Publishing

• | | Differentially private data publishing for data analysis | Dong Su | | 2016 | [:ledger:](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2220&context=open_access_dissertations) | | |
• | JTree | Differentially Private High-Dimensional Data Publication via Sampling-Based Inference | Rui Chen, Qian Xiao, Yu Zhang, Jianliang Xu | KDD | 2015 | [:ledger:](https://www.comp.hkbu.edu.hk/~xujl/Papers/kdd15.pdf) | | |
• | NoisyCut | Top-k frequent itemsets via differentially private FP-trees | Jaewoo Lee, Christopher W. Clifton | KDD | 2014 | [:ledger:](https://cybersecurity.uga.edu/publications/VI_KDD2014.pdf) | | |
• | PTT<br>k-RecursiveMedians | Differentially Private Algorithms for Empirical Machine Learning | Ben Stoddard, Yan Chen, Ashwin Machanavajjhala | CoRR | 2014 | [:ledger:](https://arxiv.org/pdf/1411.5428.pdf) | | |

Transaction Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
PATE-GAN	PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees	James Jordon, Jinsung Yoon, Mihaela van der Schaar	ICLR	2019	📒
Liancheng	Privacy as a Service: Publishing Data and Models	Ashish Dandekar, Debabrota Basu, Thomas Kister, Geong Sen Poh, Jia Xu, Stéphane Bressan	DASFAA	2019	📒
	A Data Publishing System Based on Privacy Preservation	Zhihui Wang, Yun Zhu, Xuchen Zhou	DASFAA	2019	📒
	Approximate Query Processing using Deep Generative Models	Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das	CoRR	2019	📒
G-PATE	Scalable Differentially Private Generative Student Model via PATE	Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Bo Li	CoRR	2019	📒
DP-GAN-DNN	POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network	Pei-Hsuan Lu, Chia-Mu Yu	CCS	2017	📒		⌨️
PrivBayes	PrivBayes: Private Data Release via Bayesian Networks	Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao	TODS SIGMOD	2017 2014	📒 📒		⌨️ 📷📷
	Efficient privacy-preserving temporal and spacial data aggregation for smart grid communications	Xiaolei Dong, Jun Zhou, Zhenfu Cao	Concurrency	2016	📒	📝

🔝

Streaming Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
PeGaSus	PeGaSus: Data-Adaptive Differentially Private Stream Processing	Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau	CCS	2017	📒	📝

• | CCDPSD | 异方差加噪下差分隐私流数据发布一致性优化算法 | 孙岚, 康健, 吴英杰, 张立群 | 清华大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/11.2223.N.20180921.0900.001.html) | | |
• | | 面向实时数据流的差分隐私直方图发布技术 | 杨庚, 夏春婷, 白云璐 | 南京邮电大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CJFDLAST2018&filename=NJYD201802014) | | |
• | PrivTree | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | Jun Zhang, Xiaokui Xiao, Xing Xie | SIGMOD | 2016 | [:ledger:](http://delivery.acm.org/10.1145/2890000/2882928/p155-zhang.pdf) | | |

🔝

Graph Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
GSN-DP	Geo-social network publication based on differential privacy	Xiaochun Wang, Yidong Li	FCS	2018	📒	📝	📷

🔝

Image Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
RPM	Random permutation Maxout transform for cancellable facial template protection	Andrew Beng Jin Teoh, Sejung Cho, Jihyeon Kim	MTA	2018	📒	📝
BEMK	面向人脸图像发布的差分隐私保护	张啸剑, 付聪聪, 孟小峰	JIG	2018	📒	📝
DPGAN	Differentially Private Generative Adversarial Network	Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou	CoRR	2018	📒		⌨️

🔝

Private Data Analysis

Private Learning

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Robust anomaly detection and backdoor attack detection via differential privacy	Min Du, Ruoxi Jia, Dawn Song	ICLR	2020	📒		📝⌨️
	Differentially Private Meta-Learning	Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar	ICLR	2020	📒	📝
	Privacy Enhanced Multimodal Neural Representations for Emotion Recognition	Mimansa Jaiswal, Emily Mower Provost	AAAI	2020	📒
	Utility/Privacy Trade-off through the lens of Optimal Transport	Etienne Boursier, Vianney Perchet	AISTATS	2020	📒		⌨️
	Understanding Gradient Clipping in Private SGD: A Geometric Perspective	Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong	NeurIPS	2020	📒	📝📝📝	📒
	Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy	Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii	ICML	2019	📒		📝📷📷
	A General Approach to Adding Differential Privacy to Iterative Training Procedures	H. Brendan McMahan, Galen Andrew	CoRR	2018	📒		⌨️
AdaClip	AdaCliP: Adaptive Clipping for Private SGD	Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar	CoRR	2019	📒
	Differentially Private Learning with Adaptive Clipping	Om Thakkar, Galen Andrew, H. Brendan McMahan	CoRR	2019	📒
DP-FedAvg	Learning Differentially Private Recurrent Language Models	H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang	ICLR	2018	📒	📝
	Three Tools for Practical Differential Privacy	Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini	PPML	2018	📒
dp-GAN	Differentially Private Releasing via Deep Generative Model	Xinyang Zhang, Shouling Ji, Ting Wang	CoRR	2018	📒		⌨️
	Differentially Private Federated Learning: A Client Level Perspective	Robin C. Geyer, Tassilo Klein, Moin Nabi	CoRR	2017	📒		⌨️ 💾
DPSGD	Deep Learning with Differential Privacy	Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang	CCS	2016	📒		💾 ⌨️

🔝

Frequent Itemset Mining

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Diff-FPM	Mining frequent graph patterns with differential privacy	Entong Shen, Ting Yu	KDD	2013	📒		📷

• | PrivBasis | PrivBasis: Frequent Itemset Mining with Differential Privacy | Ninghui Li, Wahbeh H. Qardaji, Dong Su, Jianneng Cao | PVLDB | 2012 | [:ledger:](https://dl.acm.org/citation.cfm?id=2350251) | | [:keyboard:](https://github.com/DongSuIBM/PrivBasis) |

🔝

Graph Data Analysis

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
FedGNN	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation	Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie	CoRR	2021	📒
Adam-DP	Privacy-Preserving Graph Convolutional Networks for Text Classification	Timour Igamberdiev, Ivan Habernal	CoRR	2021	📒
	Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models	Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha	ICML	2020	📒		📒
PPKG	Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications	Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang	CoRR	2020	📒
APGE	Adversarial Privacy Preserving Graph Embedding against Inference Attack	Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai	CoRR	2020	📒		⌨️
LPGNN	Locally Private Graph Neural Networks	Sina Sajadmanesh, Daniel Gatica-Perez	CoRR	2020	📒		⌨️

🔝

Recommender Systems

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Extended PrivSR	Towards privacy preserving social recommendation under personalized privacy settings	Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang	WWWJ	2019	📒
FedMF	Secure Federated Matrix Factorization	Di Chai, Leye Wang, Kai Chen, Qiang Yang	FML	2019	📒	📝	⌨️
GD-DR	Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy	Hyejin Shin, Sungwook Kim, Junbum Shin, Xiaokui Xiao	TKDE	2018	📒	📝
PrivSR	Personalized Privacy-Preserving Social Recommendation	Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang	AAAI	2018	📒	📝	💾
DMF	Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization	Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li	AAAI	2018	📒	📝
EpicRec	EpicRec: Towards Practical Differentially Private Framework for Personalized Recommendation	Yilin Shen, Hongxia Jin	CCS	2016	📒
DPMF	Differentially Private Matrix Factorization	Jingyu Hua, Chang Xia, Sheng Zhong	IJCAI	2015	📒	📝
DP-UnP3R	Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy	Yilin Shen, Hongxia Jin	ICDM	2014	📒

🔝

Survival Analysis

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Differentially Private Survival Function Estimation	Lovedeep Gondara, Ke Wang	MLHC	2020	📒		⌨️

🔝

Natural Language Processing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Information Leakage in Embedding Models	Congzheng Song, Ananth Raghunathan	CCS	2020	📒		📷⌨️
	Privacy Risks of General-Purpose Language Models	Xudong Pan, Mi Zhang, Shouling Ji, Min Yang	IEEE Symposium on Security and Privacy	📒
DPNR	Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness	Lingjuan Lyu, Xuanli He, Yitong Li	EMNLP	2020	📒		⌨️
TextHide	TextHide: Tackling Data Privacy for Language Understanding Tasks	Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora	EMNLP	2020	📒		⌨️
PolicyQA	PolicyQA: A Reading Comprehension Dataset for Privacy Policies	Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, Kai-Wei Chang	EMNLP	2020	📒		⌨️
FedNewsRec	Privacy-Preserving News Recommendation Model Learning	Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie	EMNLP	2020	📒		⌨️
MG-PriFair	Multimodal Review Generation with Privacy and Fairness Awareness	Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang	COLING	2020	📒		📷
	Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays	Elena Volodina, Yousuf Ali Mohammed, Sandra Derbring, Arild Matsson, Beáta Megyesi	COLING	2020	📒
OME	Towards Differentially Private Text Representations	Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao	SIGIR	2020	📒	📝	⌨️

🔝

Machine Unlearning

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	When Machine Unlearning Jeopardizes Privacy	Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang	CCS	2021	📒		⌨️📷
Mixed-Linear Forgetting	Mixed-Privacy Forgetting in Deep Networks	Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, Stefano Soatto	CVPR	2021	📒		📒
	Remember What You Want to Forget: Algorithms for Machine Unlearning	Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha Suresh	CoRR	2021	📒
GraphEraser	Graph Unlearning	Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang	CoRR	2021	📒
DeltaGrad	DeltaGrad: Rapid retraining of machine learning models	Yinjun Wu, Edgar Dobriban, Susan B. Davidson	ICML	2020	📒		📒📷📷⌨️
	Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks	Aditya Golatkar, Alessandro Achille, Stefano Soatto	CVPR	2020	📒		📒📷⌨️
	Towards Probabilistic Verification of Machine Unlearning	David Marco Sommer, Liwei Song, Sameer Wagh, Prateek Mittal	CoRR	2020	📒		⌨️
	Verifying that the influence of a user data point has been removed from a machine learning classifier	Saurabh Shintre, Jasjeet Dhaliwal	Patent	2018	📒

🔝

Cryptography

Oblivious Transfer

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Improved Private Set Intersection Against Malicious Adversaries	Peter Rindal, Mike Rosulek	EUROCRYPT	2017	📒		⌨️

🔝

Searchable Encryption

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
SPiRiT	Secure Search on Encrypted Data via Multi-Ring Sketch	Adi Akavia, Dan Feldman, Hayim Shaul	CCS	2018	📒	📝
CPABKS	Secure and Efficient Attribute-Based Encryption with Keyword Search	Haijiang Wang, Xiaolei Dong, Zhenfu Cao, Dongmei Li	CJ	2018	📒	📝

🔝

Encrypted Database

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
CryptZip	Sa Wang, Yiwen Shao, Yungang Bao	Practices of backuping homomorphically encrypted databases	FCS	2019	📒		📷
	一种基于保形加密的大数据脱敏系统实现及评估	卞超轶, 朱少敏, 周涛	电信科学	2017	📒

🔝

Encrypted Retrieval

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy	Xun Yi, Russell Paulet, Elisa Bertino, Vijay Varadharajan	TKDE	2016	📒
	A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval	Li Weng, Laurent Amsaleg, April Morton, Stéphane Marchand-Maillet	TIFS	2015	📒
	Privacy-Preserving and Content-Protecting Location Based Queries	Russell Paulet, Md. Golam Kaosar, Xun Yi, Elisa Bertino	TKDE	2014	📒

🔝

Encrypted Inference

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
CryptoNets	CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy	Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, John Wernsing	ICML	2016	📒

🔝

Awesome Federated Learning Research Materials

A curated list of awesome federated learning research materials.

Courses:

• Tutorial

Papers:

• Survey
• Algorithm
  • Communication
  • Aggregation
  • Application
• System

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
GDPR, Data Shortage and AI	Qiang Yang	HKUST	2019			📷

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Federated Machine Learning: Concept and Applications	Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong	TIST	2019	📒	📝	💾
	Federated Learning	Florian Hartmann		2018	📒		⌨️⌨️⌨️

🔝

Algorithm

Communication

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Efficient and Robust Asynchronous Federated Learning with Stragglers	Ming Chen, Bingcheng Mao, Tianyi Ma	CoRR	2020	📒

🔝

Aggregation

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
q-FedAvg	Fair Resource Allocation in Federated Learning	Tian Li, Maziar Sanjabi, Virginia Smith	ICLR	2020	📒
AFL	Agnostic Federated Learning	Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh	ICML	2019	📒		💾 📷
FedAvg	Communication-Efficient Learning of Deep Networks from Decentralized Data	Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas	AISTATS	2017	📒		💾

🔝

Application

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Federated CIFG	Federated Learning for Mobile Keyboard Prediction	Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage	CoRR	2018	📒		💾
	Federated Meta-Learning for Recommendation	Fei Chen, Zhenhua Dong, Zhenguo Li, Xiuqiang He	CoRR	2018	📒

🔝

System

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Towards Federated Learning at Scale: System Design	Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander	SysML	2019	📒

🔝

Awesome Fairness Research Materials

A curated list of awesome fairness research materials.

Courses:

• Tutorial
• Causal Fairness

Papers:

• Survey
• Statistical Fairness
• Causal Fairness
• Resource Allocation
• Others
  • Stability

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
Fairness and Control of Exposure in Two-sided Markets	Thorsten Joachims	ICTIR	2021	📒
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned	Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kıcıman, Margaret Mitchell	Microsoft Google LinkedIn	KDD 2019		📝	📷
Challenges of incorporating algorithmic fairness into industry practice	Henriette Cramer, Ken Holstein, Jenn Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright	Microsoft Research	ACM FAccT 2019			📷

🔝

Causal Fairness

	Instructors	Institution	Year	Files	Notes	Supplementaries
Counterfactual reasoning in algorithmic fairness	Ricardo Silva	UCL Alan Turing Institute	FairWare @ ICSE 2018			📷
Fairness in Machine Learning and Its Causal Aspects	Ricardo Silva	UCL Alan Turing Institute	2017			📷

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Fairness-Aware Recommendation in Multi-Sided Platforms	Masoud Mansoury	WSDM	2021	📒
	Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness	Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis	CoRR	2020	📒
	A Survey on Bias and Fairness in Machine Learning	Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan	CoRR	2019	📒
	The Frontiers of Fairness in Machine Learning	Alexandra Chouldechova, Aaron Roth	CoRR	2018	📒

🔝

Statistical Fairness

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
EquiTensors	EquiTensors: Learning Fair Integrations of Heterogeneous Urban Data	An Yan, Bill Howe	SIGMOD	2021	📒		📷⌨️
	Constructing a Fair Classifier with Generated Fair Data	Taeuk Jang, Feng Zheng, Xiaoqian Wang	AAAI	2021	📒
CFair	Conditional Learning of Fair Representations	Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon	ICLR	2020	📒	📝	📝
Fairness warnings fair-MAML	Fairness warnings and fair-MAML: learning fairly with minimal data	Dylan Slack, Sorelle A. Friedler, Emile Givental	FAT*	2020	📒		📝📷📷⌨️
AdvDebias	Inherent Tradeoffs in Learning Fair Representations	Han Zhao, Geoffrey J. Gordon	NeurIPS	2019	📒	📝	📝📝📝📷📷⌨️
Random Repair	Obtaining Fairness using Optimal Transport Theory	Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes	ICML	2019	📒		📒⌨️📷
FFVAE	Flexibly Fair Representation Learning by Disentanglement	Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel	ICML	2019	📒		📒📷
DP-postprocessing DP-oracle-learner	Differentially Private Fair Learning	Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman	ICML	2019	📒		📒📷
Fair Regression	Fair Regression: Quantitative Definitions and Reduction-Based Algorithms	Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu	ICML	2019	📒		📒⌨️⌨️📷
Pairwise Fairness	Fairness in Recommendation Ranking through Pairwise Comparisons	Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow	KDD	2019	📒	📝	📷
Strong Demographic Parity	Wasserstein Fair Classification	Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa	UAI	2019	📒		📒⌨️
	Exploring Human Gender Stereotypes with Word Association Test	Yupei Du, Yuanbin Wu, Man Lan	EMNLP	2019	📒		⌨️
AVD Penalizers SD Penalizers	Penalizing Unfairness in Binary Classification	Yahav Bechavod, Katrina Ligett	CoRR	2017	📒		⌨️
Equalized Odds	Equality of Opportunity in Supervised Learning	Moritz Hardt, Eric Price, Nati Srebro	NIPS	2016	📒		📒📝

🔝

Causal Fairness

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Counterfactual Privilege	Making Decisions that Reduce Discriminatory Impacts	Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva	ICML	2019	📒		📒⌨️ 📷📷
Fair K Fair Add	Counterfactual Fairness	Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva	NIPS	2017	📒	📝	📒⌨️📷📷
Multi-World Fairness	When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness	Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva	NIPS	2017	📒	📝	📷

🔝

Resource Allocation

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Slice Tuner	Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models	SIGMOD	2021	📒		📷📷⌨️
TFROM	TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers	Yao Wu, Jian Cao, Guandong Xu, Yudong Tan	SIGIR	2021	📒		📷
FairBatch	FairBatch: Batch Selection for Model Fairness	Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh	ICLR	2021	📒	📝	📷📷⌨️
	Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information	Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang	FAccT	2021	📒
TSFD Rank	User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets	Lequn Wang, Thorsten Joachims	ICTIR	2021	📒
EARS	Top-K Contextual Bandits with Equity of Exposure	Olivier Jeunen, Bart Goethals	RecSys	2021	📒		⌨️📷
	Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective	Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel	AIES	2021	📒
ARL	Fairness without Demographics through Adversarially Reweighted Learning	Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi	NeurIPS	2020	📒		📝📝📝📒⌨️
FairCo	Controlling Fairness and Bias in Dynamic Learning-to-Rank	Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims	SIGIR	2020	📒		📝📷⌨️
FairRec	FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms	Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty	WWW	2020	📒		⌨️📷📷
	Fair Updates in Two-Sided Market Platforms: On Incrementally Updating Recommendations	Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi	AAAI	2020	📒		📷
	Equalized odds postprocessing under imperfect group information	Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern	AISTATS	2020	📒		📒⌨️
	Fair decision making using privacy-protected data	David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau	FAT*	2020	📒		📷📷
DPSGD-F	Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy	Depeng Xu, Wei Du, Xintao Wu	CoRR	2020	📒
FEN	Learning Fairness in Multi-Agent Systems	Jiechuan Jiang, Zongqing Lu	NeurIPS	2019	📒		📒📝📝📝⌨️
	Differential Privacy Has Disparate Impact on Model Accuracy	Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov	NeurIPS	2019	📒		📝📝📝📷⌨️
DC Maximin	Group-Fairness in Influence Maximization	Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick	IJCAI	2019	📒		📝⌨️
TREE01	Fairness without Harm: Decoupled Classifiers with Preference Guarantees	Berk Ustun, Yang Liu, David C. Parkes	ICML	2019	📒		📒⌨️📷
Greedy Capture Local Capture	Proportionally Fair Clustering	Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala	ICML	2019	📒		📝⌨️📷
GF1A/B	Group Fairness for the Allocation of Indivisible Goods	Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan	AAAI	2019	📒		📝
PCCS TFCS	Fair Transfer Learning with Missing Protected Attributes	Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, Supriyo Chakraborty	AIES	2019	📒
FULTR	Fair Learning-to-Rank from Implicit Feedback	Himank Yadav, Zhengxiao Du, Thorsten Joachims	CoRR	2019	📒
	Fairness Without Demographics in Repeated Loss Minimization	Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang	ICML	2018	📒		📒⌨️

🔝

Others

Stability

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Stable-Fair	Stable and Fair Classification	Lingxiao Huang, Nisheeth K. Vishnoi	ICML	2019	📒		📝

🔝

Awesome Blockchain Research Materials

A curated list of awesome blockchain research materials.

• Survey
• Network Layer

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Yellow Paper	Ethereum: A Secure Decentralised Generalised Transaction Ledger	Dr. Gavin Wood			📒
黄皮书	以太坊：一种安全去中心化的通用交易账本	崔广斌, 高天露			📒
	Untangling Blockchain: A Data Processing View of Blockchain Systems	Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang	TKDE	2018	📒	📝	💾
	Making Sense of Blockchain Applications: A Typology for HCI	Chris Elsden, Arthi Manohar, Jo Briggs, Mike Harding, Chris Speed, John Vines	CHI	2018	📒		📝
BigchainDB	BigchainDB 2.0: The Blockchain Database	BigchainDB	BigchainDB	2018	📒		⌨️
BLOCKBENCH	BLOCKBENCH: A Framework for Analyzing Private Blockchains	Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan	SIGMOD	2017	📒		⌨️
	区块链隐私保护研究综述	祝烈煌, 高峰, 沈蒙, 李艳东, 郑宝昆, 毛洪亮, 吴震	计算机研究与发展	2017	📒	📝

🔝

Network Layer

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
BlockFL	On-Device Federated Learning via Blockchain and its Latency Analysis	Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim	CoRR	2018	📒

🔝

Other Awesome Research Materials

A curated list of awesome research materials.

• Artificial Intelligence
• Robust Statistics
• Generalization
• Large-Scale Optimization
• Combinatorial Optimization

🔝

Artificial Intelligence

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
WebFace260M	WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition	Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou	CVPR	2021	📒		💾
	Trustworthy AI	Jeannette M. Wing	Commun. ACM	2021	📒		📷
	细节决定成败：推荐系统实验反思与讨论	施韶韵, 王晨阳, 马为之, 张敏, 刘奕群, 马少平	信息安全学报	2021	📒
	Meta-Learning in Neural Networks: A Survey	Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey	CoRR	2020	📒	📝
	AI Governance in 2019 a Year in Review	Qian Shi, Hui Li, Brian Tse, John Hopcroft, Stuart Russell, Caroline Jeanmaire, Qiang Yang, Pascale Fung, Roman Yampolskiy, Allan Dafoe, Markus Anderljung, Gillian K. Hadfield, Jun Su, Thilo Hagendorff, Petra Ahrweiler, Robin Williams, Colin Allen, Poon King Wang, Ferran Jarabo Carbonell, Xiaohong Wang, Qingfeng Yang, Qi Yin, Don Wright, Miles Brundage, Jack Clark, Irene Solaiman, Gretchen Krueger, Seán Ó hÉigeartaigh, Helen Toner, Millie Liu, Steve Hoffman, Irakli Beridze, Wendell Wallach, Cyrus Hodes, Nicolas Miailhe, Jessica Cussins Newman, Dingding Chen, Eva Kaili, Francesca Rossi, Charlotte Stix, Angela Daly, Danit Gal, Arisa Ema, Goh Yihan, Nydia Remolina, Urvashi Aneja, Ying Fu, Zhiyun Zhao, Xiuquan Li, Weiwen Duan, Qun Luan, Rui Guo, Yingchun Wang	Shanghai Institute for Science of Science	2020	📒	📝
Meta-Weight-Net	Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting	Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng	NeurIPS	2019	📒	📝📝📝	📒⌨️
	Graph Neural Networks for Natural Language Processing	Shikhar Vashishth, Naganand Yadati, Partha Talukdar	EMNLP	2019		📷⌨️📷📷
	Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI	Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera	CoRR	2019	📒	📝
	以机器学习的视角来看时序点过程的最新进展	严骏驰	中国自动化学会模式识别与机器智能专业委员会通讯	2019	📒
	Temporal Point Processes and the Conditional Intensity Function	Jakob Gulddahl Rasmussen	CoRR	2018	📒
softImpute-ALS	Matrix completion and low-rank SVD via fast alternating least squares	Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh	JMLR	2015	📒		⌨️⌨️⌨️
	Efficient Per-Example Gradient Computations	Ian J. Goodfellow	CoRR	2015	📒
RBO	A similarity measure for indefinite rankings	William Webber, Alistair Moffat, Justin Zobel	TOIS	2010	📒
BPR	BPR: Bayesian Personalized Ranking from Implicit Feedback	Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme	UAI	2009	📒
	Damped Newton Algorithms for Matrix Factorization with Missing Data	A. M. Buchanan, Andrew W. Fitzgibbon	CVPR	2005	📒

🔝

Robust Statistics

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Influence Functions in Deep Learning Are Fragile	Samyadeep Basu, Phillip Pope, Soheil Feizi	ICLR	2021	📒	📝	📷
Group Influence Functions	On Second-Order Group Influence Functions for Black-Box Predictions	Samyadeep Basu, Xuchen You, Soheil Feizi	ICML	2020	📒		📒
TracIn	Estimating Training Data Influence by Tracing Gradient Descent	Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan	NeurIPS	2020	📒	📝📝📝📝📝	📒⌨️
	On the Accuracy of Influence Functions for Measuring Group Effects	Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang	NeurIPS	2019	📒	📝📝📝	📒⌨️⌨️📷
Representer Values	Representer Point Selection for Explaining Deep Neural Networks	Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar	NeurIPS	2018	📒	📝📝	📒📷⌨️
	Understanding Black-box Predictions via Influence Functions	Pang Wei Koh, Percy Liang	ICML	2017	📒		📒⌨️⌨️📷

🔝

Generalization

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	When is memorization of irrelevant training data necessary for high-accuracy learning?	Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, Kunal Talwar	STOC	2021	📒
	Understanding deep learning (still) requires rethinking generalization	Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals	CACM	2021	📒	📝	📷
	Does Learning Require Memorization? A Short Tale about a Long Tail	Vitaly Feldman	STOC	2020	📒		📒📷📷📷
	What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation	Vitaly Feldman, Chiyuan Zhang	NeurIPS	2020	📒		📒📝📝📝⌨️
	Understanding deep learning requires rethinking generalization	Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals	ICLR	2017	📒		📝⌨️📷📷

🔝

Large-Scale Optimization

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
BDA	A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton	Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang	ICML	2020	📒
AGD+	On Acceleration with Noise-Corrupted Gradients	Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia	ICML	2018	📒		💾

• Olivier Devolder, François Glineur, Yurii Nesterov: First-order methods of smooth convex optimization with inexact oracle. Math. Program. 146(1-2): 37-75 (2014)
• Alexandre d'Aspremont: Smooth Optimization with Approximate Gradient. SIAM Journal on Optimization 19(3): 1171-1183 (2008)

🔝

Combinatorial Optimization

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
GSLS	Optimizing top-k retrieval: submodularity analysis and search strategies	Chaofeng Sha, Keqiang Wang, Dell Zhang, Xiaoling Wang, Aoying Zhou	FCS WAIM	2016 2014	📒 📒
SubmEP	Ensemble Pruning: A Submodular Function Maximization Perspective	Chaofeng Sha, Keqiang Wang, Xiaoling Wang, Aoying Zhou	DASFAA	2014	📒

🔝

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